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Automated R workflow for Batman peak fitting using unified equation and stochastic modeling.

Arash Mirzahosseini1, Annamária Sepsey2, Ali Mhammad1

  • 1Department of Pharmaceutical Chemistry, Semmelweis University, Budapest, Hungary; Center for Pharmacology and Drug Research & Development, Semmelweis University, Budapest, Hungary.

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|November 21, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an automated R framework to analyze chiral drug enantiomerization kinetics using chromatographic data. The method accurately extracts kinetic and thermodynamic constants, even for complex "Batman peaks".

Keywords:
Batman peakChiral separationEnantiomersInterconversionRate constant

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Area of Science:

  • Analytical Chemistry
  • Chemical Kinetics
  • Pharmaceutical Sciences

Background:

  • Chiral drugs exhibit enantiomers with differing pharmacological effects, necessitating accurate kinetic analysis.
  • Chromatographic "Batman peaks" provide crucial data on on-column enantiomeric interconversion dynamics.

Purpose of the Study:

  • To develop an automated R framework for extracting kinetic and thermodynamic constants from chromatographic data.
  • To validate the framework's accuracy and flexibility across various chromatographic conditions.

Main Methods:

  • Integration of the unified equation and a stochastic model within an automated R workflow.
  • Application of robust optimization algorithms for accelerated and accurate data fitting.
  • Analysis of Quetiapine chromatograms under varied flow rates and temperatures.

Main Results:

  • Consistent rate constants obtained from both unified and stochastic models, aligning with optical rotation experiments.
  • The stochastic model demonstrated applicability even with coalesced peaks.
  • Determination of thermodynamic parameters via linearized Eyring-Polányi analysis.

Conclusions:

  • The developed R framework offers a robust, flexible, and customizable platform for enantiomerization kinetics determination.
  • Combining unified and stochastic modeling ensures reliable results across diverse chromatographic conditions.
  • The framework is suitable for routine analysis of dynamic enantiomerization in pharmaceutical and analytical chemistry.